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Recent Advances in Electrical & Electronic Engineering


ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Review Article

Screening Retinal Images and Extraction of the Retinal Blood Vessel for Identifying Diseases and Classification of Arteries and Veins by Using Deep Learning

Author(s): K. Susheel Kumar*, Shekhar Yadav and Nagendra Pratap Singh

Volume 16, Issue 8, 2023

Published on: 21 December, 2022

Page: [790 - 804] Pages: 15

DOI: 10.2174/2352096516666221124111107

Price: $65


In recent years, the extraction of retinal blood vessels from low contrast retinal images has become a challenging task for diagnosing retinal diseases such as Diabetic Retinopathy, Agerelated Macular Degeneration (AMD), Retinopathy of Prematurity (ROP), cataract, and glaucoma. Another challenge is screening the retinal image to identify the disease early on. However, data analysis from a large population-based study of retinal diseases is required to help resolve the uncertainty in identifying the retinal disease based on retinal image classification using deep learning approaches from the retinal diseases dataset. Therefore, we proposed the survey on the deep learning approach for screening the retinal image to identify the early stages of the disease and discussed retinal disease analysis based on deep learning approaches to detect Diabetic Retinopathy, AMD ROP, and Glaucoma. We also discuss deep learning applications in the segmentation of retinal blood vessels, extraction of the optic disc, optic cup, and fovea, and OCT segmentation to detect retinal disease for diagnosis of diseases. Finally, discuss the classification of arteries/veins using a deep learning approach.

Keywords: Retinal image analysis, segmentation, optic disc, optic cup, fovea detection, OCT segmentation.

Graphical Abstract
J. Ambati, and B.J. Fowler, "Mechanisms of age-related macular degeneration", Neuron, vol. 75, no. 1, pp. 26-39, 2012.
[] [PMID: 22794258]
A.R. Shah, and T.W. Gardner, "Diabetic retinopathy: research to clinical practice", Clin. Diabetes Endocrinol., vol. 3, no. 1, p. 9, 2017.
[] [PMID: 29075511]
N.M. Bressler, "Age-related macular degeneration is the leading cause of blindness", JAMA, vol. 291, no. 15, pp. 1900-1901, 2004.
[] [PMID: 15108691]
R. Lee, T.Y. Wong, and C. Sabanayagam, "Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss", Eye Vis., vol. 2, no. 1, p. 17, 2015.
[] [PMID: 26605370]
A. London, I. Benhar, and M. Schwartz, "The retina as a window to the brain-from eye research to CNS disorders", Nat. Rev. Neurol., vol. 9, no. 1, pp. 44-53, 2013.
[] [PMID: 23165340]
I. Chatterjee, "Feature selection technique for time-series fmri data of schizophrenia patients", Zenodo, 2018. Available from:
I. Chatterjee, M. Agarwal, B. Rana, N. Lakhyani, and N. Kumar, "Bi-objective approach for computer-aided diagnosis of schizophrenia patients using fMRI data", Multimedia Tools Appl., vol. 77, no. 20, pp. 26991-27015, 2018.
A.S. Matthews, "Authoritarian ruling elites database (ared)", 2019. Available from:
R. Tiwari, M. Husain, S. Gupta, and A. Srivastava, "Improving ant colony optimization algorithm for data clustering", In: Proceedings of the International Conference and Workshop on Emerging Trends in Technology, Feb 26, 2010, New York, NY, United States, 2010, pp. 529-534.
V. Verma, and R.K. Aggarwal, "A comparative analysis of similarity measures akin to the Jaccard index in collaborative recommendations: empirical and theoretical perspective", Soc. Netw. Anal. Min., vol. 10, no. 1, p. 43, 2020.
A.M.S. Mahdy, K.A. Gepreel, K. Lotfy, and A.A. El-Bary, "A numerical method for solving the Rubella ailment disease model", Int. J. Mod. Phys. C, vol. 32, no. 7, p. 2150097, 2021.
A.M.S. Mahdy, M.S. Mohamed, K. Lotfy, M. Alhazmi, A.A. El-Bary, and M.H. Raddadi, "Numerical solution and dynamical behaviors for solving fractional nonlinear Rubella ailment disease model", Results Phys., vol. 24, p. 104091, 2021.
A.M. Mahdy, M. Higazy, and M.S. Mohamed, "Optimal and memristor-based control of a nonlinear fractional tumor-immune model", Materials & Continua, vol. 67, no. 3, pp. 3463-3486, 2021.
A.M.S. Mahdy, and M. Higazy, "Numerical different methods for solving the nonlinear biochemical reaction model", Int. J. Appl. Comput. Math., vol. 5, no. 6, p. 148, 2019.
K.A. Gepree, A.M.S. Mahdy, M.S. Mohamed, and A. Al-Amiri, "Reduced differential transform method for solving nonlinear biomathematics models", Comput. Mater. Continua, vol. 61, no. 3, pp. 979-994, 2019.
M. Higazy, A. El-Mesady, A.M.S. Mahdy, S. Ullah, and A. Al-Ghamdi, "Numerical, approximate solutions, and optimal control on the deathly lassa hemorrhagic fever disease in pregnant women", J. Funct. Spaces, vol. 2021, pp. 1-15, 2021.
A.M.S. Mahdy, "A numerical method for solving the nonlinear equations of Emden-Fowler models", J. Ocean. Engineer. Sci., vol. 2, p. 44, 2022.
A.M.S. Mahdy, "Numerical solutions for solving model time‐fractional Fokker–Planck equation", Numer. Methods Partial Differ. Equ., vol. 37, no. 2, pp. 1120-1135, 2021.
A.M.S. Mahdy, K. Lotfy, and A.A. El-Bary, "Use of optimal control in studying the dynamical behaviors of fractional financial awareness models", Soft Comput., vol. 26, no. 7, pp. 3401-3409, 2022.
A.M.S. Mahdy, A.S. Mohamed, and A.A.H. Mtawa, "Sumudu decomposition method for solving fractional-order Logistic differential equation", J. Adv. Mathe, vol. 10, no. 7, 2015.
J. Guo, H. Gao, Z. Liu, F. Huang, J. Zhang, X. Li, and J. Ma, "ICRA: An Intelligent Clustering Routing Approach for UAV Ad Hoc Networks", IEEE Trans. Intell. Transp. Syst., pp. 1-14, 2022.
J. Guo, X. Li, Z. Liu, J. Ma, C. Yang, J. Zhang, and D. Wu, "TROVE: A context-awareness trust model for VANETs using reinforcement learning", IEEE Internet Things J., vol. 7, no. 7, pp. 6647-6662, 2020.
L.M. Ruta, D.J. Magliano, R. LeMesurier, H.R. Taylor, P.Z. Zimmet, and J.E. Shaw, "Prevalence of diabetic retinopathy in Type 2 diabetes in developing and developed countries", Diabet. Med., vol. 30, no. 4, pp. 387-398, 2013.
[] [PMID: 23331210]
O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation", In: N. Navab, J. Hornegger, W. Wells, A. Frangi, Eds., Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science., vol. 9351. Springer: Cham, 2015.
World Health Organization, Diabetic retinopathy screening: a short guide: increase effectiveness, maximize benefits and minimize harm. 2020. Available from:
M. Pekala, N. Joshi, T.Y.A. Liu, N.M. Bressler, D.C. DeBuc, and P. Burlina, "Deep learning based retinal OCT segmentation", Comput. Biol. Med., vol. 114, p. 103445, 2019.
[] [PMID: 31561100]
P.M. Burlina, N. Joshi, M. Pekala, K.D. Pacheco, D.E. Freund, and N.M. Bressler, "Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks", JAMA Ophthalmol., vol. 135, no. 11, pp. 1170-1176, 2017.
[] [PMID: 28973096]
S. Chakraborty, D. Shukla, B. Mishra, and S. Singh, "Lipid – An emerging platform for oral delivery of drugs with poor bioavailability", Eur. J. Pharm. Biopharm., vol. 73, no. 1, pp. 1-15, 2009.
[] [PMID: 19505572]
K.S. Kumar, and N.P. Singh, "Analysis of retinal blood vessel segmentation techniques: a systematic survey", Multimedia Tools Appl., vol. 2022, pp. 1-55, 2022.
M. Pandey, V.K. Pathak, and B.D. Chaudhary, "A framework for interest-based community evolution and sharing of latent knowledge", Inter. J. Grid Utility Comp., vol. 3, no. 2/3, pp. 200-213, 2012.
R. Singh, and A. Khare, "Fusion of multimodal medical images using Daubechies complex wavelet transform – A multiresolution approach", Inf. Fusion, vol. 19, pp. 49-60, 2014.
R. Srivastava, and A. Daniel, Efficient model of cloud trustworthiness for selecting services using fuzzy logic.Emerging Technologies in Data Mining and Information Security., Springer, 2019, pp. 249-260.
V. Gulshan, L. Peng, M. Coram, M.C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P.C. Nelson, J.L. Mega, and D.R. Webster, "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs", JAMA, vol. 316, no. 22, pp. 2402-2410, 2016.
[] [PMID: 27898976]
P. Burlina, A. Galdran, and P. Costa, Arti_cial intelligence and deep learning in retinal image analysis. Computational Retinal Image Analysis., Elsevier, 2019, pp. 379-404.
A. Krizhevsky, I. Sutskever, and G.E. Hinton, "Imagenet classification with deep convolutional neural networks", Adv. Neural Inf. Process. Syst., vol. 25, pp. 1097-1105, 2012.
Q Hu, MD Abramoff, and MK Garvin, "Automated construction of arterial and venous trees in retinal images", J. Med. Imag., vol. 2, no. 4, pp. 044-001, 2015.
K. Simonyan, and A. Zisserman, "Very deep convolutional networks for large-scale image recognition", arXiv, p. 14091556, 2014.
C.A. Ludwig, C. Perera, D. Myung, M.A. Greven, S.J. Smith, R.T. Chang, and T. Leng, "Automatic identification of referral warranted diabetic retinopathy using deep learning on mobile phone images", Transl. Vis. Sci. Technol., vol. 9, no. 2, p. 60, 2020.
[] [PMID: 33294301]
G.U. Nneji, J. Cai, J. Deng, H.N. Monday, M.A. Hossin, and S. Nahar, "Identification of diabetic retinopathy using weighted fusion deep learning based on dual-channel fundus scans", Diagnostics, vol. 12, no. 2, p. 540, 2022.
[] [PMID: 35204628]
D. Das, S.K. Biswas, and S. Bandyopadhyay, "A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning", Multimedia Tools Appl., vol. 81, no. 18, pp. 25613-25655, 2022.
[] [PMID: 35342328]
P. Khojasteh, B. Aliahmad, and D.K. Kumar, "Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms", BMC Ophthalmology., vol. 18, pp. 1-13, 2018.
H. Pratt, F. Coenen, D.M. Broadbent, S.P. Harding, and Y. Zheng, "Convolutional neural networks for diabetic retinopathy", Procedia Comput. Sci., vol. 90, pp. 200-205, 2016.
K. Xu, D. Feng, and H. Mi, "Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image", Molecules, vol. 22, no. 12, p. 2054, 2017.
[] [PMID: 29168750]
M. Chetoui, and M.A. Akhloufi, "Explainable diabetic retinopathy using EfficientNET", In 2020 42nd annual international conference of the IEEE engineering in Medicine & Biology Society (EMBC)., Jul 20-24, 2020, Montreal, QC, Canada, 2020, pp. 1966-1969
S. Dutta, B.C.S. Manideep, S.M. Basha, R.D. Caytiles, and N.C.S.N. Iyengar, "Classification of diabetic retinopathy images by using deep learning models", Int. J. Grid Distrib. Comput., vol. 11, no. 1, pp. 89-106, 2018.
G. Quellec, K. Charrière, Y. Boudi, B. Cochener, and M. Lamard, "Deep image mining for diabetic retinopathy screening", Med. Image Anal., vol. 39, pp. 178-193, 2017.
[] [PMID: 28511066]
C. Iwendi, S. Khan, J.H. Anajemba, M. Mittal, M. Alenezi, and M. Alazab, "The use of ensemble models for multiple class and binary class classification for improving intrusion detection systems", Sensors, vol. 20, no. 9, p. 2559, 2020.
[] [PMID: 32365937]
J. Lee, Y.K. Kim, K.H. Park, and J.W. Jeoung, "Diagnosing Glaucoma with spectral-domain optical coherence tomography using deep learning classifier", J. Glaucoma, vol. 29, no. 4, pp. 287-294, 2020.
[] [PMID: 32053552]
J.I. Orlando, E. Prokofyeva, M. del Fresno, and M.B. Blaschko, "An ensemble deep learning based approach for red lesion detection in fundus images", Comput. Methods Programs Biomed., vol. 153, pp. 115-127, 2018.
[] [PMID: 29157445]
B. Tymchenko, P. Marchenko, and D. Spodarets, "Deep learning approach to diabetic retinopathy detection", arXiv, pp. 1-9, 2020.
D.S.W. Ting, C.Y.L. Cheung, G. Lim, G.S.W. Tan, N.D. Quang, A. Gan, H. Hamzah, R. Garcia-Franco, I.Y. San Yeo, S.Y. Lee, E.Y.M. Wong, C. Sabanayagam, M. Baskaran, F. Ibrahim, N.C. Tan, E.A. Finkelstein, E.L. Lamoureux, I.Y. Wong, N.M. Bressler, S. Sivaprasad, R. Varma, J.B. Jonas, M.G. He, C.Y. Cheng, G.C.M. Cheung, T. Aung, W. Hsu, M.L. Lee, and T.Y. Wong, "Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes", JAMA, vol. 318, no. 22, pp. 2211-2223, 2017.
[] [PMID: 29234807]
P. Srivastava, and A. Khare, "Integration of wavelet transform, Local Binary Patterns and moments for content-based image retrieval", J. Vis. Commun. Image Represent., vol. 42, pp. 78-103, 2017.
P. Burlina, K.D. Pacheco, N. Joshi, D.E. Freund, and N.M. Bressler, "Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis", Comput. Biol. Med., vol. 82, pp. 80-86, 2017.
[] [PMID: 28167406]
J.M. Brown, J.P. Campbell, A. Beers, K. Chang, S. Ostmo, R.V.P. Chan, J. Dy, D. Erdogmus, S. Ioannidis, J. Kalpathy-Cramer, and M.F. Chiang, "Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks", JAMA Ophthalmol., vol. 136, no. 7, pp. 803-810, 2018.
[] [PMID: 29801159]
F. Grassmann, J. Mengelkamp, C. Brandl, S. Harsch, M.E. Zimmermann, B. Linkohr, A. Peters, I.M. Heid, C. Palm, and B.H.F. Weber, "A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography", Ophthalmology, vol. 125, no. 9, pp. 1410-1420, 2018.
[] [PMID: 29653860]
F. Fumero, J. Sigut, and S. Alayon, "Interactive tool and database for optic disc and cup segmentation of stereo and monocular retinal fundus images", In: 23rd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, vol: Short papers proceedings, 2015, pp. 91-97.
D.E. Worrall, C.M. Wilson, and G.J. Brostow, "Automated retinopathy of prematurity case detection with convolutional neural net-works", In: Deep learning and data labelling for medical applications., Springer, 2016, pp. 68-76.
J. Hu, Y. Chen, J. Zhong, R. Ju, and Z. Yi, "Automated analysis for retinopathy of prematurity by deep neural networks", IEEE Trans. Med. Imaging, vol. 38, no. 1, pp. 269-279, 2019.
[] [PMID: 30080144]
A. Lang, A. Carass, M. Hauser, E.S. Sotirchos, P.A. Calabresi, H.S. Ying, and J.L. Prince, "Retinal layer segmentation of macular OCT images using boundary classification", Biomed. Opt. Express, vol. 4, no. 7, pp. 1133-1152, 2013.
[] [PMID: 23847738]
M.M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A.R. Rudnicka, C.G. Owen, and S.A. Barman, "Blood vessel segmentation methodologies in retinal images – A survey", Comput. Methods Programs Biomed., vol. 108, no. 1, pp. 407-433, 2012.
[] [PMID: 22525589]
J.J. Wang, "Retinal vessel diameters and obesity: a population based study in older persons", Obesity, vol. 14, no. 2, pp. 206-214, 2006.
M. Foracchia, E. Grisan, and A. Ruggeri, "Extraction and quantitative description of vessel features in hypertensive retinopathy fundus images ", In: Book Abstracts 2nd International Workshop on Computer Assisted Fundus Image Analysis, 2001.
P. Mitchell, H. Leung, J.J. Wang, E. Rochtchina, A.J. Lee, T.Y. Wong, and R. Klein, "Retinal vessel diameter and open-angle glaucoma", Ophthalmology, vol. 112, no. 2, pp. 245-250, 2005.
[] [PMID: 15691558]
K. Goatman, A. Charnley, and L. Webster, "Assessment of automated disease detection in diabetic retinopathy screening using two-_eld photography", PLOS one, vol. 6, no. 12, pp. e27-524, 2011.
J. Staal, M.D. Abràmoff, M. Niemeijer, M.A. Viergever, and B. van Ginneken, "Ridge-based vessel segmentation in color images of the retina", IEEE Trans. Med. Imaging, vol. 23, no. 4, pp. 501-509, 2004.
[] [PMID: 15084075]
P. Liskowski, and K. Krawiec, "Segmenting retinal blood vessels with deep neural networks", IEEE Trans. Med. Imaging, vol. 35, no. 11, pp. 2369-2380, 2016.
[] [PMID: 27046869]
Y. Lin, H. Zhang, and G. Hu, "Automatic retinal vessel segmentation via deeply supervised and smoothly regularized network", IEEE Access, vol. 7, pp. 57717-57724, 2019.
K.K. Maninis, and J. Pont-Tuset, "Deep Retinal Image Understanding", In: S. Ourselin, L. Joskowicz, M. Sabuncu, G. Unal, W. Wells, Eds., Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, Lecture Notes in Computer Science., vol. 9901. Springer: Cham, 2016.
H. Fu, Y. Xu, and S. Lin, "DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field", In: S. Ourselin, L. Joskowicz, M. Sabuncu, G. Unal, W. Wells, Eds., Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. Lecture Notes in Computer Science., vol. 9901. Springer: Cham, 2016.
J. Mo, and L. Zhang, "Multi-level deep supervised networks for retinal vessel segmentation", Int. J. CARS, vol. 12, no. 12, pp. 2181-2193, 2017.
[] [PMID: 28577175]
H. Zhao, and N. Sun, "Improved U-Net Model for Nerve Segmentation", In: Y. Zhao, X. Kong, D. Taubman, Eds., Image and Graphics. ICIG 2017. Lecture Notes in Computer Science, vol. 10667. Springer: Cham, 2017.
H. Zhao, H. Li, S. Maurer-Stroh, Y. Guo, Q. Deng, and L. Cheng, "Supervised segmentation of un-annotated retinal fundus images by synthesis", IEEE Trans. Med. Imaging, vol. 38, no. 1, pp. 46-56, 2019.
[] [PMID: 30047872]
Z. Yan, X. Yang, and K.T. Cheng, "A three-stage deep learning model for accurate retinal vessel segmentation", IEEE J. Biomed. Health Inform., vol. 23, no. 4, pp. 1427-1436, 2019.
[] [PMID: 30281503]
Y. Wu, Y. Xia, and Y. Song, "Multiscale Network Followed Network Model for Retinal Vessel Segmentation", In: A. Frangi, J. Schnabel, C. Davatzikos, C. Alberola-López, G. Fichtinger, Eds., Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Lecture Notes in Computer Science., vol. 11071. Springer: Cham, 2018.
R.A. Welikala, P.J. Foster, P.H. Whincup, A.R. Rudnicka, C.G. Owen, D.P. Strachan, and S.A. Barman, "Automated arteriole and venule classification using deep learning for retinal images from the UK Biobank cohort", Comput. Biol. Med., vol. 90, pp. 23-32, 2017.
[] [PMID: 28917120]
M.I. Meyer, A. Galdran, and P. Costa, "Deep Convolutional Artery/Vein Classification of Retinal Vessels", In: A. Campilho, F. Karray, B. ter Haar Romeny, Eds., Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science, vol. 10882. Springer: Cham, 2018.
X. Xu, R. Wang, P. Lv, B. Gao, C. Li, Z. Tian, T. Tan, and F. Xu, "Simultaneous arteriole and venule segmentation with domain-specific loss function on a new public database", Biomed. Opt. Express, vol. 9, no. 7, pp. 3153-3166, 2018.
[] [PMID: 29984089]
A. Galdran, M. Meyer, and P. Costa, "Uncertainty-aware artery/vein classification on retinal images", In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), IEEE, Apr 11-18, 2019, Venice, Italy, 2019, pp. 556-560
H. Fu, J. Cheng, Y. Xu, D.W.K. Wong, J. Liu, and X. Cao, "Joint optic disc and cup segmentation based on multi-label deep network and polar transformation", IEEE Trans. Med. Imaging, vol. 37, no. 7, pp. 1597-1605, 2018.
[] [PMID: 29969410]
J. Tian, B. Varga, E. Tatrai, P. Fanni, G.M. Somfai, W.E. Smiddy, and D.C. Debuc, "Performance evaluation of automated segmentation software on optical coherence tomography volume data", J. Biophotonics, vol. 9, no. 5, pp. 478-489, 2016.
[] [PMID: 27159849]
J.G. Zilly, and J.M. Buhmann, "Boosting convolutional _filters with entropy sampling for optic cup and disc image segmentation from fundus images", In: International workshop on machine learning in medical imaging, Springer, 2015, pp. 136-143.
Z. Gu, P. Liu, and K. Zhou, Deepdisc: Optic disc segmentation based on atrous convolution and spatial pyramid pooling.Computational Pathology and Ophthalmic Medical Image Analysis., Springer, 2018, pp. 253-260.
Y. Liu, D. Fu, Z. Huang, and H. Tong, "Optic disc segmentation in fundus images using adversarial training", IET Image Process., vol. 13, no. 2, pp. 375-381, 2019.
X. Sun, Y. Xu, and W. Zhao, "Optic disc segmentation from retinal fundus images via deep object detection networks", In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society, Jul 18-21, 2018, Honolulu, HI, USA, 2018, pp. 5954-5957.
H. Fu, J. Cheng, Y. Xu, C. Zhang, D.W.K. Wong, J. Liu, and X. Cao, "Disc-aware ensemble network for glaucoma screening from fundus image", IEEE Trans. Med. Imaging, vol. 37, no. 11, pp. 2493-2501, 2018.
[] [PMID: 29994764]
S. Sedai, R. Tennakoon, and P. Roy, "Multi-stage segmentation of the fovea in retinal fundus images using fully convolutional neural networks", In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), April 18-21, 2017, Melbourne, VIC, Australia, 2017, pp. 1083-1086.
B. Al-Bander, W. Al-Nuaimy, B.M. Williams, and Y. Zheng, "Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc", Biomed. Signal Process. Control, vol. 40, pp. 91-101, 2018.
M.I. Meyer, and A. Galdran, "A Pixel-Wise Distance Regression Approach for Joint Retinal Optical Disc and Fovea Detection", In: A. Frangi, J. Schnabel, C. Davatzikos, C. Alberola-López, G. Fichtinger, Eds., Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Lecture Notes in Computer Science., vol. 11071. Springer: Cham, 2018.
T. Aratiujo, G. Aresta, and A. Galdran, "Uolo-automatic object detection and segmentation in biomedical images", In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support., Springer, 2018, pp. 165-173.
S.J. Chiu, M.J. Allingham, P.S. Mettu, S.W. Cousins, J.A. Izatt, and S. Farsiu, "Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema", Biomed. Opt. Express, vol. 6, no. 4, pp. 1172-1194, 2015.
[] [PMID: 25909003]
L. Fang, D. Cunefare, C. Wang, R.H. Guymer, S. Li, and S. Farsiu, "Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search", Biomed. Opt. Express, vol. 8, no. 5, pp. 2732-2744, 2017.
[] [PMID: 28663902]
A.G. Roy, S. Conjeti, S.P.K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, "ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks", Biomed. Opt. Express, vol. 8, no. 8, pp. 3627-3642, 2017.
[] [PMID: 28856040]
S. Krishna Devalla, J.M. Mari, and T.A. Tun, "A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head", arXiv, 1707.07609, 2017.
C.S. Lee, A.J. Tyring, N.P. Deruyter, Y. Wu, A. Rokem, and A.Y. Lee, "Deep-learning based, automated segmentation of macular edema in optical coherence tomography", Biomed. Opt. Express, vol. 8, no. 7, pp. 3440-3448, 2017.
[] [PMID: 28717579]
Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, "A cross-modality learning approach for vessel segmentation in retinal images", IEEE Trans. Med. Imaging, vol. 35, no. 1, pp. 109-118, 2016.
[] [PMID: 26208306]
Z. Yan, X. Yang, and K.T. Cheng, "Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation", IEEE Trans. Biomed. Eng., vol. 65, no. 9, pp. 1912-1923, 2018.
[] [PMID: 29993396]
Y. Ruan, J. Xue, and T. Li, "Multi phase level set algorithm based on fully convolutional networks (fcn-mls) for retinal layer segmentation in sd-Oct images with central serous chorioretinopathy (csc)", Biomedical. Optics. Express, vol. 10, no. 8, pp. 3987-4002, 2019.
D. Xiang, G. Chen, F. Shi, W. Zhu, Q. Liu, S. Yuan, and X. Chen, "Automatic retinal layer segmentation of oct images with central serous retinopathy", IEEE J. Biomed. Health Inform., vol. 23, no. 1, pp. 283-295, 2019.
[] [PMID: 29994379]
K. Gao, S. Niu, Z. Ji, M. Wu, Q. Chen, R. Xu, S. Yuan, W. Fan, Y. Chen, and J. Dong, "Double-branched and area-constraint fully convolutional networks for automated serous retinal detachment segmentation in SD-OCT images", Comput. Methods Programs Biomed., vol. 176, pp. 69-80, 2019.
[] [PMID: 31200913]
J. Novosel, Z. Wang, and H. De Jong, "Locally-adaptive loosely-coupled level sets for retinal layer and fluid segmentation in subjects with central serous retinopathy", In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Apr 13-16, 2016, Prague, Czech Republic, 2016, pp. 702-705.
A.M. Syed, T. Hassan, M.U. Akram, S. Naz, and S. Khalid, "Automated diagnosis of macular edema and central serous retinopathy through robust reconstruction of 3D retinal surfaces", Comput. Methods Programs Biomed., vol. 137, pp. 1-10, 2016.
[] [PMID: 28110716]
S. Khalid, M.U. Akram, T. Hassan, A. Nasim, and A. Jameel, "Fully automated robust system to detect retinal edema, central serous chorioretinopathy, and age related macular degeneration from optical coherence tomography images", BioMed Res. Int., vol. 2017, pp. 1-15, 2017.
[] [PMID: 28424788]
J. Yoon, J. Han, J.I. Park, J.S. Hwang, J.M. Han, J. Sohn, K.H. Park, and D.D.J. Hwang, "Optical coherence tomography-based deep-learning model for detecting central serous chorioretinopathy", Sci. Rep., vol. 10, no. 1, p. 18852, 2020.
[] [PMID: 33139813]
F. Girard, and F. Cheriet, "Artery/vein classification in fundus images using cnn and likelihood score propagation", In: 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2017, pp. 720-724.
J. Tian, B. Varga, and G.M. Somfai, "Real-time automatic segmentation of optical coherence tomography volume data of the macular region", PloS one, vol. 10, no. 8, pp. e0133-908, 2015.
F. Girard, C. Kavalec, and F. Cheriet, "Joint segmentation and classification of retinal arteries/veins from fundus images", Artif. Intell. Med., vol. 94, pp. 96-109, 2019.
[] [PMID: 30871687]
R. Hemelings, B. Elen, and I. Stalmans, "Artery-vein segmentation in fundus images using a fully convolutional network", Comp. Medi. Imag. Graphics, vol. 76, pp. 101-636, 2019.
W. Ma, S. Yu, and K. Ma, "Multi-task neural networks with spatial activation for retinal vessel segmentation and artery/vein classification", In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, vol. 11764. Springer: Cham, 2019.
J. Yang, X. Dong, Y. Hu, Q. Peng, G. Tao, Y. Ou, H. Cai, and X. Yang, "Fully automatic arteriovenous segmentation in retinal images via topology-aware generative adversarial networks", Interdiscip. Sci., vol. 12, no. 3, pp. 323-334, 2020.
[] [PMID: 32725575]
T.D. Keenan, S. Dharssi, Y. Peng, Q. Chen, E. Agrón, W.T. Wong, Z. Lu, and E.Y. Chew, "A deep learning approach for automated detection of geographic atrophy from color fundus photographs", Ophthalmology, vol. 126, no. 11, pp. 1533-1540, 2019.
[] [PMID: 31358385]
M.A. Zapata, D. Royo-Fibla, O. Font, J.I. Vela, I. Marcantonio, E.U. Moya-Sánchez, A. Sánchez-Pérez, D. Garcia-Gasulla, U. Cortés, E. Ayguadé, and J. Labarta, "Artificial intelligence to identify retinal fundus images, quality validation, laterality evaluation, macular degeneration, and suspected glaucoma", Clin. Ophthalmol., vol. 14, pp. 419-429, 2020.
[] [PMID: 32103888]
S. Keel, Z. Li, J. Scheetz, L. Robman, J. Phung, G. Makeyeva, K. Aung, C. Liu, X. Yan, W. Meng, R. Guymer, R. Chang, and M. He, "Development and validation of a deep‐learning algorithm for the detection of neovascular age‐related macular degeneration from colour fundus photographs", Clin. Exp. Ophthalmol., vol. 47, no. 8, pp. 1009-1018, 2019.
[] [PMID: 31215760]
C. González-Gonzalo, V. Sánchez-Gutiérrez, P. Hernández-Martínez, I. Contreras, Y.T. Lechanteur, A. Domanian, B. Ginneken, and C.I. Sánchez, "Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age‐related macular degeneration", Acta Ophthalmol., vol. 98, no. 4, pp. 368-377, 2020.
[] [PMID: 31773912]
A. Bhuiyan, T.Y. Wong, D.S.W. Ting, A. Govindaiah, E.H. Souied, and R.T. Smith, "Artificial intelligence to stratify severity of age-related macular degeneration (AMD) and predict risk of progression to late AMD", Transl. Vis. Sci. Technol., vol. 9, no. 2, pp. 25-25, 2020.
[] [PMID: 32818086]
A. Govindaiah, R.T. Smith, and A. Bhuiyan, "A new and improved method for automated screening of age-related macular degeneration using ensemble deep neural networks", Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., vol. 2018, pp. 702-705, 2018.
[] [PMID: 30440493]
Y. Peng, S. Dharssi, Q. Chen, T.D. Keenan, E. Agrón, W.T. Wong, E.Y. Chew, and Z. Lu, "DeepSeeNet: a deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs", Ophthalmology, vol. 126, no. 4, pp. 565-575, 2019.
[] [PMID: 30471319]
M.R.K. Mookiah, S. Hogg, T.J. MacGillivray, V. Prathiba, R. Pradeepa, V. Mohan, R.M. Anjana, A.S. Doney, C.N.A. Palmer, and E. Trucco, "A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification", Med. Image Anal., vol. 68, p. 101905, 2021.
[] [PMID: 33385700]
Q. Mirsharif, F. Tajeripour, and H. Pourreza, "Automated characterization of blood vessels as arteries and veins in retinal images", Comput. Med. Imaging Graph., vol. 37, no. 7-8, pp. 607-617, 2013.
[] [PMID: 23849699]
B. Dashtbozorg, A.M. Mendonca, and A. Campilho, "Automatic estimation of the arteriolar-to-venular ratio in retinal images using a graph-based approach for artery/vein classification", In: M. Kamel, A. Campilho, Eds., Image Analysis and Recognition. ICIAR 2013. Lecture Notes in Computer Science, vol. 7950. Springer: Berlin, Heidelberg, .
F. Huang, B. Dashtbozorg, and B.M.H. Romeny, "Artery/vein classification using reflection features in retina fundus images", Mach. Vis. Appl., vol. 29, no. 1, pp. 23-34, 2018.
S. Akbar, M.U. Akram, M. Sharif, A. Tariq, and U. Yasin, "Arteriovenous ratio and papilledema based hybrid decision support system for detection and grading of hypertensive retinopathy", Comput. Methods Programs Biomed., vol. 154, pp. 123-141, 2018.
[] [PMID: 29249337]
V. Vijayakumar, D.D. Koozekanani, and R. White, "Classifiers fusion for improved vessel recognition with application in quantification of generalized arteriolar narrowing", J. Innov. Opt. Health Sci., vol. 13, no. 1, pp. 1-34, 2019.
X. Xu, T. Tan, and F. Xu, "An improved u-net architecture for simultaneous arteriole and venule segmentation in fundus image", In: M. Nixon, S. Mahmoodi, R. Zwiggelaar, Eds., Medical Image Understanding and Analysis. Communications in Computer and Information Science., vol. Vol. 894. Springer: Cham, 2018.
Y. Zhao, J. Zhao, J. Yang, Y. Liu, Y. Zhao, Y. Zheng, L. Xia, and Y. Wang, "Saliency driven vasculature segmentation with infinite perimeter active contour model", Neurocomputing, vol. 259, pp. 201-209, 2017.
V.S. Joshi, J.M. Reinhardt, and M.K. Garvin, "Automated method for identification and artery-venous classification of vessel trees in retinal vessel networks", PloS one, vol. 9, no. 2, pp. e88-061, 2014.
B.J. Zou, Y. Chen, C.Z. Zhu, Z-L. Chen, and Z-Q. Zhang, "Supervised vessels classification based on feature selection", J. Comput. Sci. Technol., vol. 32, no. 6, pp. 1222-1230, 2017.
X. Yin, S. Irshad, and Y. Zhang, "Classifiers fusion for improved vessel recognition with application in quantification of generalized arteriolar narrowing", J. Innov. Opt. Health Sci., vol. 13, no. 01, pp. 1950-021, 2020.
X. Yang, C. Liu, H. Le Minh, Z. Wang, A. Chien, and K.T.T. Cheng, "An automated method for accurate vessel segmentation", Phys. Med. Biol., vol. 62, no. 9, pp. 3757-3778, 2017.
[] [PMID: 28384126]
K. Eppenhof, E. Bekkers, and T.T. Berendschot, "Retinal artery/vein classi_cation via graph cut optimization", OMIA, Munich, Germany, Iowa Research Online, pp. 121-128, 2015.

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